帮助用户做出信息披露决策:适应的潜力

Bart P. Knijnenburg, A. Kobsa
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引用次数: 39

摘要

个性化依赖于每个用户的个人数据。用户通常不愿意透露自己的信息,也不愿意被系统“跟踪”。我们调查了在隐私文献中提出的不同类型的披露理由(理由)是否会增加用户泄露自己的人口统计和上下文信息的意愿,并提高他们对系统的满意度。根据文献中的发现,我们还研究了请求顺序的影响。我们对手机应用推荐模型的实验表明,不存在适合所有人的最佳策略。启发式可以定义为为每个用户选择最有效的理由来提高披露或满意度,考虑到用户的性别,披露倾向和征求个人信息的类型。我们将讨论这些发现对研究的意义,这些研究旨在为每个用户个性化隐私策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Helping users with information disclosure decisions: potential for adaptation
Personalization relies on personal data about each individual user. Users are quite often reluctant though to disclose information about themselves and to be "tracked" by a system. We investigated whether different types of rationales (justifications) for disclosure that have been suggested in the privacy literature would increase users' willingness to divulge demographic and contextual information about themselves, and would raise their satisfaction with the system. We also looked at the effect of the order of requests, owing to findings from the literature. Our experiment with a mockup of a mobile app recommender shows that there is no single strategy that is optimal for everyone. Heuristics can be defined though that select for each user the most effective justification to raise disclosure or satisfaction, taking the user's gender, disclosure tendency, and the type of solicited personal information into account. We discuss the implications of these findings for research aimed at personalizing privacy strategies to each individual user.
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